Recent large scale genomic studies have identified and confirmed numerous recurrent single nucleotide variations and aneuploidies in invasive breast cancer (IBC). In contrast to IBC, little is understood about the genomic changes associated with progression to breast cancer, from normal tissue to early neoplasias to ductal carcinoma in situ (DCIS) to IBC. The clinical evaluation of DCIS found in screening breast biopsies relies solely on morphologic criteria and the light microscope that were developed decades ago. These morphologic criteria do not perform well in identifying DCIS lesions that are associated with or are destined to progress to IBC. Among newly diagnosed breast cancer cases in the United States, 20% will be DCIS. With the advent of screening mammography, there has been a remarkable rise in the diagnosis of DCIS among asymptomatic women without the expected reduction in breast cancer mortality, leading to concerns about both over- diagnosis and over-treatment. Clinical trials are emerging to analyze active surveillance as a clinical strategy for patients screen detected with low grade DCIS. Many patients with screen detected DCIS are now left in a quandary, wondering what is their risk of progression to IBC if their lesion were left untreated at initial detection. Because IBC represents the accumulation of recurrent, common genetic changes, we hypothesize that the identification of these mutations, the degree of their accumulation in DCIS, and associated nuclear changes will help us identify cases that have a high likelihood of progressing to IBC. This would allow us to stratify these lesions for risk of developing IBC. We have demonstrated the feasibility of this approach with preliminary data from three independent studies: one involving whole genome sequencing of neoplasms in the progression to IBC, a second involving targeted DNA copy number measurements in a cohort of DCIS, and a third examining the nuclear morphometric differences between DCIS and hyperplasias. We propose a case-control study design using three distinct longitudinal cohorts. We will perform targeted analysis of genomic and nuclear phenotypic features with a case-control study design on two large independent cohorts, including cohorts from the Nurses' Health Study (NHS), Washington University (WashU), and a smaller cohort from Stanford University (SU) to create Discovery, Training and Cross-validation, and Test sets. In our discovery cohort, we will use whole exome sequencing, FISH, and nuclear morphometric analysis to identify genomic and phenotypic changes in DCIS that develop IBC versus cases of DCIS that do not develop IBC. Biomarkers identified from these studies will be used to construct a genomic predictor of breast cancer risk in DCIS. The predictive model will be built using DCIS samples from cases and controls in the Nurses' Health Study. The genomic predictor of future IBC risk generated in the NHS will then be validated on an independent large cohort of DCIS samples with long-term clinical follow-up from Washington University. Improved knowledge of risk stratification for DCIS will help reduce IBC incidence and mortality by improving our ability o stratify patients with DCIS into molecularly defined subgroups of high- and low-risk patients. The high-risk patients may benefit from more aggressive clinical treatment, like complete surgical excision, chemoprophylaxis, and/or intensive surveillance with techniques such as breast MRI, while the low-risk patients may not require or benefit from these measures.